Create app.py

app.py

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+import os
+import sys
+import types
+import importlib.machinery
+from typing import List, Dict
+
+import gradio as gr
+import torch
+from PIL import Image
+
+# =========================
+# 1) 偽裝 flash_attn（避免硬相依）
+# =========================
+def _make_pkg_stub(fullname: str):
+    m = types.ModuleType(fullname)
+    m.__file__ = f"<stub {fullname}>"
+    m.__package__ = fullname.rpartition('.')[0]
+    m.__path__ = []
+    m.__spec__ = importlib.machinery.ModuleSpec(fullname, loader=None, is_package=True)
+    sys.modules[fullname] = m
+    return m
+
+for name in [
+    "flash_attn",
+    "flash_attn.ops",
+    "flash_attn.layers",
+    "flash_attn.functional",
+    "flash_attn.bert_padding",
+    "flash_attn.flash_attn_interface",
+]:
+    if name not in sys.modules:
+        _make_pkg_stub(name)
+
+# =========================
+# 2) Florence-2 載入（eager + dtype 對齊 + 關 cache）
+# =========================
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+MODEL_ID = os.getenv("MODEL_ID", "microsoft/Florence-2-base")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+TASK_TOKENS = {
+    "caption": "<CAPTION>",
+    "object_detection": "<OBJECT_DETECTION>",
+}
+
+_processor = None
+_model = None
+
+def get_florence2():
+    global _processor, _model
+    if _processor is None or _model is None:
+        _processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+        _model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            trust_remote_code=True,
+            attn_implementation="eager",
+            torch_dtype=torch.float16 if device == "cuda" else torch.float32
+        ).to(device).eval()
+        # 關掉快取，避免某些環境下 beam/cache 造成錯誤
+        _model.config.use_cache = False
+    return _processor, _model
+
+@torch.inference_mode()
+def florence2_text(image: Image.Image, task: str = "caption"):
+    proc, mdl = get_florence2()
+    token = TASK_TOKENS.get(task, "<CAPTION>")
+    text = token  # 這兩個任務都是「不帶額外輸入」
+
+    batch = proc(text=text, images=image, return_tensors="pt")
+    inputs = {}
+    for k, v in batch.items():
+        if isinstance(v, torch.Tensor):
+            if v.is_floating_point():
+                inputs[k] = v.to(device=device, dtype=mdl.dtype)
+            else:
+                inputs[k] = v.to(device=device)
+        else:
+            inputs[k] = v
+
+    ids = mdl.generate(
+        **inputs,
+        max_new_tokens=128,
+        do_sample=False,
+        num_beams=1,       # 用貪婪生成以提升穩定性
+        use_cache=False,   # 關 cache 避免空 past_key_values 問題
+        early_stopping=False,
+        eos_token_id=getattr(getattr(proc, "tokenizer", None), "eos_token_id", None),
+    )
+    out = proc.batch_decode(ids, skip_special_tokens=True)[0].strip()
+    if ">" in out:
+        out = out.split(">", 1)[-1].strip()
+    return out
+
+# =========================
+# 3) 營養資料 / 同義詞 / 規則
+# =========================
+FOOD_DB = {
+    "rice":  {"kcal":130, "carb_g":28, "protein_g":2.4, "fat_g":0.3, "sodium_mg":0,   "cat":"全榖雜糧類", "base_g":150, "tip":"主食可改糙米/全穀增加膳食纖維"},
+    "noodles":{"kcal":138, "carb_g":25, "protein_g":4.5, "fat_g":1.9, "sodium_mg":170, "cat":"全榖雜糧類", "base_g":180, "tip":"清湯少油，避免重鹹湯底"},
+    "bread": {"kcal":265, "carb_g":49, "protein_g":9.0, "fat_g":3.2, "sodium_mg":490, "cat":"全榖雜糧類", "base_g":60,  "tip":"可選全麥減少抹醬、甜餡"},
+    "broccoli":{"kcal":35, "carb_g":7, "protein_g":2.4, "fat_g":0.4, "sodium_mg":33,  "cat":"蔬菜類",     "base_g":80,  "tip":"川燙/清炒保留口感與維生素"},
+    "spinach":{"kcal":23, "carb_g":3.6,"protein_g":2.9,"fat_g":0.4,"sodium_mg":70,    "cat":"蔬菜類",     "base_g":80,  "tip":"川燙後快炒，少鹽少油"},
+    "chicken":{"kcal":215,"carb_g":0,  "protein_g":27, "fat_g":12, "sodium_mg":90,    "cat":"豆魚蛋肉類", "base_g":120, "tip":"去皮烹調、烤/氣炸取代油炸"},
+    "soy_braised_chicken_leg":{"kcal":220,"carb_g":0,"protein_g":24,"fat_g":12,"sodium_mg":550,"cat":"豆魚蛋肉類","base_g":130,"tip":"減醬油與滷汁、可先汆燙再滷"},
+    "salmon":{"kcal":208,"carb_g":0,  "protein_g":20, "fat_g":13, "sodium_mg":60,     "cat":"豆魚蛋肉類", "base_g":120, "tip":"烤/蒸保留 Omega-3，少鹽少醬"},
+    "pork_chop":{"kcal":242,"carb_g":0,"protein_g":27,"fat_g":14,"sodium_mg":75,      "cat":"豆魚蛋肉類", "base_g":120, "tip":"少裹粉油炸，改煎烤並瀝油"},
+    "tofu":  {"kcal":76, "carb_g":1.9,"protein_g":8.1,"fat_g":4.8,"sodium_mg":7,      "cat":"豆魚蛋肉類", "base_g":120, "tip":"少勾芡、少滷汁，清蒸清爽"},
+    "egg":   {"kcal":155,"carb_g":1.1,"protein_g":13, "fat_g":11, "sodium_mg":124,    "cat":"豆魚蛋肉類", "base_g":60,  "tip":"水煮/荷包少油，避免重鹹醬料"},
+    "banana":{"kcal":89, "carb_g":23, "protein_g":1.1,"fat_g":0.3,"sodium_mg":1,      "cat":"水果類",     "base_g":100, "tip":"控制份量，避免一次過量"},
+    "miso_soup":{"kcal":36,"carb_g":4.3,"protein_g":2.0,"fat_g":1.3,"sodium_mg":550,  "cat":"湯品/飲品",  "base_g":200, "tip":"味噌湯偏鹹，建議少量品嚐"},
+    # 泛化：若你想讓 salad/fish 直接有數值，可打開下列兩筆
+    # "salad": {"kcal":30,"carb_g":5,"protein_g":1.5,"fat_g":0.5,"sodium_mg":40,"cat":"蔬菜類","base_g":100,"tip":"少醬少油，優先清爽調味"},
+    # "fish":  {"kcal":170,"carb_g":0,"protein_g":22,"fat_g":8,"sodium_mg":70,"cat":"豆魚蛋肉類","base_g":120,"tip":"蒸/烤/煎少油，避免重鹹醬汁"},
+}
+
+ALIASES = {
+    "white rice":"rice","steamed rice":"rice","飯":"rice","白飯":"rice",
+    "麵":"noodles","拉麵":"noodles","麵條":"noodles","義大利麵":"noodles",
+    "麵包":"bread","吐司":"bread",
+    "雞肉":"chicken","雞胸":"chicken","烤雞":"chicken",
+    "滷雞腿":"soy_braised_chicken_leg","醬油雞腿":"soy_braised_chicken_leg",
+    "鮭魚":"salmon","三文魚":"salmon",
+    "豬排":"pork_chop",
+    "豆腐":"tofu",
+    "蛋":"egg","水煮蛋":"egg","荷包蛋":"egg",
+    "花椰菜":"broccoli","青花菜":"broccoli","菠菜":"spinach",
+    "香蕉":"banana","味噌湯":"miso_soup",
+}
+
+RULES = {"T2DM": {"carb_g_per_meal_max": 60}, "HTN": {"sodium_mg_per_meal_max": 600}}
+PORTION_MUL = {"小":0.8, "中":1.0, "大":1.2}
+DEFAULT_BASE_G = 100
+
+# 類別對應（泛稱 → 類別）
+GENERIC_TO_CATEGORY = {
+    "vegetable":"蔬菜類","vegetables":"蔬菜類","greens":"蔬菜類","salad":"蔬菜類",
+    "meat":"豆魚蛋肉類","seafood":"豆魚蛋肉類","fish":"豆魚蛋肉類",
+    "noodles":"全榖雜糧類","bread":"全榖雜糧類","rice":"全榖雜糧類",
+    "soup":"湯品/飲品","drink":"湯品/飲品","beverage":"湯品/飲品"
+}
+
+# =========================
+# 4) 文本抽詞（偵測文字 → 食物詞）
+# =========================
+import re
+
+# 砍掉「環境尾巴」：on (top of) a table / tray / desk ...
+ENV_TAIL = re.compile(
+    r"\b(on\s+(?:top\s+of\s+)?(?:a|the)?\s*(?:table|tray|desk|counter|tabletop))\b.*$",
+    flags=re.I
+)
+
+# 停用詞/顏色/器皿/常見形容詞
+STOPWORDS = {
+    "a","an","the","with","and","of","on","in","to","served","over","side","sides",
+    "set","dish","meal","mixed","assorted","fresh","hot","cold","topped","style","seasoned",
+    # 中文
+    "便當","套餐","一盤","一碗","配菜","附餐","湯","沙拉","醬","佐","搭配","附","拌","炒","滷","炸","烤","蒸","煮"
+}
+COLOR_WORDS = {"white","black","red","green","yellow","orange","brown","purple","pink","golden"}
+UTENSILS = {"plate","bowl","tray","box","cup","glass","container","table","desk","counter","tabletop"}
+ADJ_MISC = {"filled","placed","served","topped","layered","mixed","assorted","piece","slice","fillet","serving"}
+
+FOOD_LIKE = {
+    "salad","vegetable","vegetables","greens","meat","seafood","fish",
+    "chicken","beef","pork","shrimp","tofu","egg",
+    "rice","noodles","bread","soup","fruit","fruits"
+}
+
+def detect_foods_from_text(text: str) -> List[str]:
+    lower = text.lower()
+    labels = set()
+    for k in FOOD_DB.keys():
+        if k in lower:
+            labels.add(k)
+    for alias, key in ALIASES.items():
+        if alias in text or alias.lower() in lower:
+            labels.add(key)
+    return list(labels)
+
+def extract_food_terms_free(text: str) -> List[str]:
+    """
+    從偵測／描述文字中抽食物詞（允許未知）：
+    - 砍掉環境尾巴（on top of a table ...）
+    - 切片（, ; . and with），過濾顏色/器皿/形容詞/停用詞
+    - 取片尾名詞；再補 FOOD_LIKE 名詞掃描
+    - alias 映射 → 主鍵；未知則保留原字
+    """
+    t = text.strip().lower()
+    t = ENV_TAIL.sub("", t)
+
+    hits = set()
+
+    # 解析「X of Y」→ 優先抓 Y（e.g., piece of fish → fish）
+    for pat in [r"(?:piece|slice|fillet|serving)\s+of\s+([a-z\u4e00-\u9fff]+)"]:
+        for m in re.findall(pat, t, flags=re.I):
+            y = m.strip()
+            if y in COLOR_WORDS or y in UTENSILS or y in ADJ_MISC or y in STOPWORDS:
+                continue
+            hits.add(ALIASES.get(y, y))
+
+    parts = re.split(r"(?:,|;|\.|\band\b|\bwith\b|\n)+", t, flags=re.I)
+    for p in parts:
+        if not p:
+            continue
+        toks = re.findall(r"[a-z\u4e00-\u9fff]+", p)
+        toks = [
+            w for w in toks
+            if w not in COLOR_WORDS
+            and w not in UTENSILS
+            and w not in ADJ_MISC
+            and w not in STOPWORDS
+            and len(w) >= 2
+        ]
+        if not toks:
+            continue
+        head = toks[-1]
+        hits.add(ALIASES.get(head, head))
+
+    for w in FOOD_LIKE:
+        if re.search(rf"\b{re.escape(w)}\b", t):
+            hits.add(ALIASES.get(w, w))
+
+    return list(hits)
+
+# =========================
+# 5) 估重 / 營養 / 規則
+# =========================
+def estimate_weight(name: str, plate_cm: int, portion: str) -> int:
+    base = FOOD_DB.get(name, {}).get("base_g", DEFAULT_BASE_G)
+    mul = PORTION_MUL.get(portion, 1.0)
+    grams = int(base * mul * (plate_cm / 24))
+    return max(10, grams)
+
+def grams_to_nutrition(name: str, grams: int) -> Dict:
+    info = FOOD_DB[name]
+    ratio = grams / 100.0
+    out = {"name": name, "cat": info["cat"], "weight_g": grams, "tip": info.get("tip","")}
+    for k in ("kcal","carb_g","protein_g","fat_g","sodium_mg"):
+        out[k] = round(info[k] * ratio, 1)
+    return out
+
+def make_placeholder_item(name: str, plate_cm: int, portion: str, cat: str = "未分類"):
+    grams = int(DEFAULT_BASE_G * (plate_cm / 24) * PORTION_MUL.get(portion, 1.0))
+    return {
+        "name": name, "cat": cat, "weight_g": grams,
+        "kcal": "待新增資訊", "carb_g": "待新增資訊", "protein_g": "待新增資訊",
+        "fat_g": "待新增資訊", "sodium_mg": "待新增資訊", "tip": "待新增資訊"
+    }
+
+def eval_rules(items: List[Dict], conditions: List[str]):
+    totals = {}
+    for it in items:
+        if isinstance(it.get("kcal"), (int, float)):
+            for k in ("kcal","carb_g","protein_g","fat_g","sodium_mg"):
+                totals[k] = round(totals.get(k,0) + float(it[k]), 1)
+    advice = []
+    if "T2DM" in conditions and totals.get("carb_g",0) > RULES["T2DM"]["carb_g_per_meal_max"]:
+        advice.append("【糖尿病】碳水偏高，建議主食減量或改全穀。")
+    if "HTN" in conditions and totals.get("sodium_mg",0) > RULES["HTN"]["sodium_mg_per_meal_max"]:
+        advice.append("【高血壓】鈉含量偏高，少鹽、避免重口味與滷味/湯品。")
+    cats = {}
+    for it in items:
+        cats[it["cat"]] = cats.get(it["cat"], 0) + 1
+    return totals, advice, cats
+
+# =========================
+# 6) Pipeline（偵測為主，Caption 顯示）
+# =========================
+def run_pipeline(image, plate_cm, portion, conditions, task_mode, dev_mode):
+    if image is None:
+        return "請先上傳一張照片。", "", [], {}
+
+    # 先用偵測任務決定清單來源
+    if dev_mode:
+        det_txt = "rice, vegetables, grilled chicken"
+    else:
+        det_txt = florence2_text(image, task="object_detection")
+
+    # 再跑 caption 只用來顯示（不影響清單）
+    if dev_mode:
+        cap_txt = "A bento with white rice, broccoli and grilled chicken thigh."
+    else:
+        cap_txt = florence2_text(image, task="caption")
+
+    src_text = det_txt  # 清單來源固定用偵測文字
+    labels_known = detect_foods_from_text(src_text)
+    labels_free  = extract_food_terms_free(src_text)
+
+    labels_all, seen = [], set()
+    for term in labels_free + labels_known:
+        key = ALIASES.get(term, term)
+        if key not in seen:
+            labels_all.append(key); seen.add(key)
+
+    items = []
+    for name in labels_all[:6]:
+        if name in FOOD_DB:
+            g = estimate_weight(name, plate_cm, portion)
+            items.append(grams_to_nutrition(name, g))
+        else:
+            cat = GENERIC_TO_CATEGORY.get(name, "未分類")
+            items.append(make_placeholder_item(name, plate_cm, portion, cat=cat))
+
+    totals, advice, cats = eval_rules([it for it in items if isinstance(it.get("kcal"), (int,float))], conditions)
+
+    # 組輸出：顯示偵測 + 描述；清單以偵測為準
+    labels_display = [it["name"] for it in items]
+    lines = [
+        f"模型輸出（偵測）：{det_txt}",
+        f"模型輸出（描述）：{cap_txt}",
+        ""
+    ]
+    if labels_display:
+        lines.append("偵測到： " + ", ".join(labels_display))
+    else:
+        lines.append("偵測到： (無)")
+    lines.append("")
+    for it in items:
+        kcal = it['kcal'] if isinstance(it['kcal'], (int, float)) else it['kcal']
+        carb = it['carb_g'] if isinstance(it['carb_g'], (int, float)) else it['carb_g']
+        prot = it['protein_g'] if isinstance(it['protein_g'], (int, float)) else it['protein_g']
+        fat  = it['fat_g'] if isinstance(it['fat_g'], (int, float)) else it['fat_g']
+        na   = it['sodium_mg'] if isinstance(it['sodium_mg'], (int, float)) else it['sodium_mg']
+        lines.append(f"- {it['name']} ({it['cat']}) {it['weight_g']} g → "
+                     f"{kcal} kcal, C{carb} g, P{prot} g, F{fat} g, Na{na} mg")
+
+    if totals:
+        lines.append("")
+        lines.append(f"總計：{totals.get('kcal',0)} kcal，碳水 {totals.get('carb_g',0)} g，蛋白 {totals.get('protein_g',0)} g，脂肪 {totals.get('fat_g',0)} g，鈉 {totals.get('sodium_mg',0)} mg")
+    if advice:
+        lines.append("建議：" + " ".join(advice))
+
+    # 「模型原始輸出」欄位顯示 caption（較好讀）
+    return "\n".join(lines), cap_txt, items, totals
+
+# =========================
+# 7) Gradio 介面
+# =========================
+with gr.Blocks(title="FoodAI · Florence-2 Demo") as demo:
+    gr.Markdown("# 🍱 FoodAI · Florence-2 Demo\n上傳餐點 → 偵測（主）/描述（輔） → 估營養/建議\n\n> 開發模式：不跑模型，固定假字串���便測試 UI/流程。")
+    with gr.Row():
+        with gr.Column(scale=1):
+            img = gr.Image(type="pil", label="上傳圖片")
+            plate = gr.Slider(18, 28, value=24, step=1, label="盤子直徑 (cm)")
+            portion = gr.Radio(["小", "中", "大"], value="中", label="份量")
+            cond = gr.CheckboxGroup(["T2DM", "HTN"], label="狀況")
+            # 預設改為「偵測」
+            task_mode = gr.Radio(["描述 (Caption)", "偵測 (Object Detection)"], value="偵測 (Object Detection)", label="任務")
+            dev_mode = gr.Checkbox(label="開發模式（不跑模型）", value=False)
+            btn = gr.Button("開始分析", variant="primary")
+        with gr.Column(scale=1):
+            out_md = gr.Markdown(label="結果")
+            raw = gr.Textbox(label="模型原始輸出（Caption）", lines=4)
+            js = gr.JSON(label="逐項結果")
+            total = gr.JSON(label="總計")
+
+    btn.click(run_pipeline, inputs=[img, plate, portion, cond, task_mode, dev_mode], outputs=[out_md, raw, js, total])
+
+if __name__ == "__main__":
+    PORT = int(os.getenv("PORT", "7860"))
+    demo.launch(server_name="0.0.0.0", server_port=PORT)